1. Field of the Invention
The present invention relates to wireless localization and communications technology. More specifically, the present invention is applicable to improving localization accuracy and communications performance in wireless communication systems.
2. Discussion of the Related Art
Because of its very wide bandwidth, ultra-wideband (UWB) technology promises accurate ranging and localization systems capable of resolving individual multipath components (MPCs). Using UWB technology, the time-of-arrival (TOA) of the received signal can be estimated with high accuracy when the first arriving path can be correctly identified. Various systems using UWB technology have been disclosed, including those disclosed in the articles: (a) “Analysis of undetected direct path in time of arrival based UWB indoor geolocation,” by B. Alavi and K. Pahlavan, published in Proc. IEEE Vehic. Technol. Conf. (VTC), vol. 4, Dallas, Tex., September 2005, pp. 2627-2631; (b) “Non-coherent TOA estimation in IR-UWB systems with different signal waveforms,” by I. Guvenc, Z. Sahinoglu, A. F. Molisch, and P. Orlik, published in in Proc. IEEE Int. Workshop on Ultrawideband Networks (UWBNETS), Boston, Mass., October 2005, pp. 245-251, (invited paper); (c) “Analysis of threshold-based TOA estimators in UWB channels,” by D. Dardari, C. C. Chong, and M. Z. Win, published in the 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, September 2006, (Invited Paper); and (d) “Improved lower bounds on time of arrival estimation error in UWB realistic channels,” by D. Dardari, C. C. Chong and M. Z. Win, published in IEEE Intl. Conf. on Ultra-Wideband (ICUWB 2006), Waltham, Mass., USA, September 2006 (Invited Paper).
One challenge for a localization system is to successfully mitigate non-line-of-sight (NLOS) effects. When the direct path between an anchor node (AN) and a mobile terminal is obstructed, the TOA of the signal to the AN is delayed, which introduces a positive bias. NLOS TOA estimates adversely affect localization accuracy. Hence, prior art cellular networks typically identify the ANs that are under NLOS conditions and mitigate their effects. For example, the article “The non-line of sight problem in mobile location estimation,” by M. P. Wylie and J. Holtzman, published in Proc. IEEE Int. Conf. Universal Personal Commun., Cambridge, Mass., September 1996, pp. 827-831, teaches comparing the standard deviation of range measurements to a threshold for NLOS signal identification, when the measurement noise variance is known. Similarly, the article “Decision theoretic framework for NLOS identification,” by J. Borras, P. Hatrack, and N. B. Mandayam, “published in Proc. IEEE Vehicular Technol. Conf. (VTC), vol. 2, Ontario, Canada, May 1998, pp. 1583-1587, discloses a decision-theoretic NLOS identification framework using various hypothesis tests for known and unknown probability density functions (PDFs) of the TOA measurements.
The article “Non-parametric non-line-of-sight identification,” by S. Gezici, H. Kobayashi, and H. V. Poor, published in Proc. IEEE Vehic. Technol. Conf. (VTC), vol. 4, Orlando, Fla., October 2003, pp. 2544-2548, discloses a non-parametric NLOS identification approach, which allows the probability density functions of the TOA measurements to be approximated. A suitable distance metric is used between the known measurement noise distribution and the non-parametrically estimated measurement distribution.
These prior art NLOS identification techniques all assume that the TOA measurements for NLOS base stations (BSs) change over time. Such an assumption is reasonable for a moving terminal, for which the TOA measurements have a larger variance. However, when the terminal is static (e.g., in wireless personal area network (WPAN) applications), the distribution of the NLOS measurements may show little deviation from the distribution under LOS condition. There, the multipath characteristics of the received signal provide insight useful for LOS/NLOS identification. For example, European Patent Application Publication EP 1,469,685, entitled “A method distinguishing line of sight (LOS) from non-line-of-sight (NLOS) in CDMA mobile communication system,” by X. Diao and F. Guo, filed on Mar. 29, 2003, published on Oct. 20, 2004, discloses that a received code division multiple access (CDMA) signal is LOS if: 1) the power ratio of the global maximum path to the local maximum path is greater than a given threshold, and 2) the arrival time difference between the first path and the maximum path is less than a given time interval. Similarly, the article “ML time-of-arrival estimation based on low complexity UWB energy detection,” by Rabbachin, I. Oppermann, and B. Denis, published in Proc. IEEE Int. Conf. Ultrawideband (ICUWB), Waltham, Mass., September 2006, discloses that the NLOS identification for UWB systems may be performed by comparing the normalized strongest path with a fixed threshold. In either scheme, judicious parameter selection (e.g., the threshold or the time interval) is essential.
As an alternative to identifying NLOS conditions from the received multipath signal, information derived from the overall mobile network may be used to mitigate NLOS conditions. For example, the article “A non-line-of-sight error mitigation algorithm in location estimation,” by P. C. Chen, published in Proc. IEEE Int. Conf. Wireless Commun. Networking (WCNC), vol. 1, New Orleans, La., September 1999, pp. 316-320, discloses a residual-based algorithm for NLOS mitigation. That algorithm is based on three or more available base stations, using location estimates and residuals for different combinations of base stations. (When all the nodes are LOS, three base stations are required to perform a two-dimensional (2-D) localization, while four base stations are required to perform a 3-dimensional (3-D) localization.) The location estimates with smaller residuals are more likely to represent the correct terminal location. Hence, the technique disclosed in the article weights the different location estimates inversely with to the corresponding residuals.
Other NLOS mitigation techniques using information derived from the mobile network are disclosed in (a) “Robust estimator for non-line-of-sight error mitigation in indoor localization,” by R. Casas, A. Marco, J. J. Guerrero, and J. Falco, published in Eurasip J. Applied Sig. Processing, pp. 1-8, 2006; (b) “Time-of-arrival based localization under NLOS conditions,” by Y. T. Chan, W. Y. Tsui, H. C. So, and P. C. Ching, published in IEEE Trans. Vehic. Technol., vol. 55, no. 1, pp. 17-24, January 2006; (c) “A database method to mitigate the NLOS error in mobile phone positioning,” by B. Li, A. G. Dempster, and C. Rizos, published in Proc. IEEE Position Location and Navigation Symposium (PLANS), San Diego, Calif., April 2006; (d) “An iterative NLOS mitigation algorithm for location estimation in sensor networks,” by X. Li, published in Proc. IST Mobile and Wireless Commun. Summit, Myconos, Greece, June 2006; (e) “Non-line-of-sight error mitigation in mobile location,” by L. Cong and W. Zhuang, published in Proc. IEEE INFOCOM, Hong Kong, March 2004, pp. 650-659; (f) “A non-line-of-sight mitigation technique based on ML-detection,” by J. Riba and A. Urruela, published in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, Quebec, Canada, May 2004, pp. 153-156; (g) “A linear programming approach to NLOS error mitigation in sensor networks,” by S. Venkatesh and R. M. Buehrer, published in Proc. IEEE IPSN, Nashville, Tenn., April 2006; (h) “An efficient geometry-constrained location estimation algorithm for NLOS environments,” by C. L. Chen and K. T. Feng, published in Proc. IEEE Int. Conf. Wireless Networks, Commun., Mobile Computing, Hawaii, USA, June 2005, pp. 244-249; and (i) “A TOA based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation,” by X. Wang, Z. Wang, and B. O. Dea, published in IEEE Trans. Vehic. Technol., vol. 52, no. 1, pp. 112-116, January 2003.
However, some of these NLOS mitigation and identification techniques use only information from the mobile network, and do not take advantage of information in the received signal. Other techniques which take into consideration statistics of the measured distances require the distance measurements to be recorded. Typically, a large number of real-time measurements are required for an accurate characterization of LOS and NLOS conditions. Also, under these techniques, the NLOS conditions can be identified only when the terminal is mobile, thus allowing the measured NLOS bias to show a variation.
European Patent Application Publication EP 1,469,685 discloses a method that uses the multipath components of the received signal in a CDMA system. This technique takes advantage only of the delay information in the strongest path, and the ratio between the global and local maximum paths. The technique relies on appropriately selecting thresholds for these parameters.
One use of channel statistics in LOS/NLOS identification of UWB signals is briefly discussed in the article “ML time-of-arrival estimation based on low complexity UWB energy detection,” by Rabbachin et al., discussed above. The Rabbachin article compares a (normalized) strongest path with a threshold for LOS/NLOS identification. Rabbachin's technique requires accurately determining an optimal threshold, and does not take advantage of the information in the received signal, except for the information in the strongest path.